# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import mindspore.context as context
import mindspore.nn as nn
import numpy as onp
import pytest
import scipy as osp
from mindspore.common import dtype as mstype

from mindspore import Tensor
from mindspore import _checkparam as validator
from mindspore.ops import PrimitiveWithInfer
from mindspore.ops import prim_attr_register

context.set_context(mode=context.GRAPH_MODE, device_target='CPU')


class QR(PrimitiveWithInfer):
    """
    QR decomposition
    A = Q.R
    """

    @prim_attr_register
    def __init__(self, mode: str = "full"):
        super().__init__(name="QR")
        self.mode = validator.check_value_type("mode", mode, [str], self.name)

        self.init_prim_io_names(inputs=['x'], outputs=['q', 'r'])

    def __infer__(self, x):
        x_shape = x['shape']
        x_dtype = x['dtype']
        m, n = x_shape
        if self.mode == "economic":
            q_shape = (m, min(m, n))
            r_shape = (min(m, n), n)
        else:
            q_shape = (m, m)
            r_shape = (m, n)

        output = {
            'shape': (q_shape, r_shape),
            'dtype': (x_dtype, x_dtype),
            'value': None
        }
        return output

    def infer_dtype(self, x_dtype):
        validator.check_tensor_dtype_valid(x_dtype, [mstype.float32, mstype.float64], self.name, True)
        return x_dtype


def _match_array(actual, expected, error=0):
    if isinstance(actual, int):
        actual = onp.asarray(actual)
    if isinstance(actual, tuple):
        actual = onp.asarray(actual)

    if error > 0:
        onp.testing.assert_almost_equal(actual, expected, decimal=error)
    else:
        onp.testing.assert_equal(actual, expected)


class QRNet(nn.Cell):
    def __init__(self, mode: str = "full"):
        super(QRNet, self).__init__()
        self.mode = mode
        self.qr = QR(mode=self.mode)

    def construct(self, a):
        q, r = self.qr(a)
        if self.mode == 'r':
            return (r,)
        return q, r


@pytest.mark.parametrize('a_shape', [(9, 6), (6, 9)])
@pytest.mark.parametrize('dtype', [onp.float32, onp.float64])
@pytest.mark.parametrize('mode', ['full', 'r', 'economic'])
def test_lu_net(a_shape, dtype, mode):
    """
    Feature: ALL To ALL
    Description: test cases for qr decomposition test cases for A = Q.R
    Expectation: the result match to scipy
    """
    onp.random.seed(0)

    if mode == 'r':
        m, n = a_shape
        a = onp.random.random((m, n)).astype(dtype)
        osp_r = osp.linalg.qr(a, mode=mode)

        msp_qr = QRNet(mode=mode)
        tensor_a = Tensor(a)
        msp_r = msp_qr(tensor_a)

        _match_array(msp_r[0].asnumpy(), osp_r[0], error=5)
    else:
        m, n = a_shape
        a = onp.random.random((m, n)).astype(dtype)
        osp_q, osp_r = osp.linalg.qr(a, mode=mode)

        msp_qr = QRNet(mode=mode)
        tensor_a = Tensor(a)
        msp_q, msp_r = msp_qr(tensor_a)

        _match_array(msp_q.asnumpy(), osp_q, error=5)
        _match_array(msp_r.asnumpy(), osp_r, error=5)
